Recommender Systems in Requirements Engineering

The process can result in massive amounts of noisy and semistructured data that must be analyzed and distilled in order to extract useful requirements. As a result, many human-intensive tasks in requirements elicitation, analysis, and management processes can be augmented and supported through the use of recommender system and machine-learning techniques. In this article we describe several areas in which recommendation technologies have been applied to the requirements engineering domain, namely stakeholder identification, domain analysis, requirements elicitation, and decision support across several requirements analysis and prioritization tasks. We also highlight ongoing challenges and opportunities for applying recommender systems in the requirements engineering domain. These activities engage various stakeholders in the task of identifying and producing an agreed-upon set of requirements that clearly specify the functionality, behavior, and constraints of the proposed system.